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Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers

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Abstract

Background

Tumor endothelial cells (TECs) play a significant role in regulating the tumor microenvironment, drug response, and immune cell activities in various cancers. However, the association between TEC gene expression signature and patient prognosis or therapeutic response remains poorly understood.

Methods

We analyzed transcriptomics data of normal and tumor endothelial cells obtained from the GEO database to identify differentially expressed genes (DEGs) associated with TECs. We then compared these DEGs with those commonly found across five different tumor types from the TCGA database to determine their prognostic relevance. Using these genes, we constructed a prognostic risk model integrated with clinical features to develop a nomogram model, which we validated through biological experiments.

Results

We identified 12 TEC-related prognostic genes across multiple tumor types, of which five genes were sufficient to construct a prognostic risk model with an AUC of 0.682. The risk scores effectively predicted patient prognosis and immunotherapeutic response. Our newly developed nomogram model provided more accurate prognostic estimates of cancer patients than the TNM staging method (AUC = 0.735) and was validated using external patient cohorts. Finally, RT–PCR and immunohistochemical analyses indicated that the expression of these 5 TEC-related prognostic genes was up-regulated in both patient-derived tumors and cancer cell lines, while depletion of the hub genes reduced cancer cell growth, migration and invasion, and enhanced their sensitivity to gemcitabine or cytarabine.

Conclusions

Our study discovered the first TEC-related gene expression signature that can be used to construct a prognostic risk model for guiding treatment options in multiple cancers.

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Data availability

The data supporting the findings of this study are given in the main manuscript and supplementary files.

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Acknowledgements

We thanked Professor K. Hodivala-Dilke for her technical advice.

Funding

This work was supported by the Grants from the National Natural Science Foundation of China (81920108028, 82272830, 82102755); the Science and Technology Program of Guangzhou (201904020008); Guangdong Science and Technology Department (2020A0505100029, 2020B1212060018, 2020B1212030004).

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Author notes

  1. Xianhua Zhuo and Cheng Huang have contributed equally to this work.

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China

    Xianhua Zhuo, Cheng Huang, Liangping Su, Qiuping Xu & Ping-Pui Wong

  2. Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China

    Cheng Huang, Liangping Su, Qiuping Xu & Ping-Pui Wong

  3. Department of Otolaryngology, Head and Neck Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China

    Xianhua Zhuo, Faya Liang, Wenqian Xie, Ping Han & Xiaoming Huang

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  1. Xianhua Zhuo
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Contributions

XHZ and CH performed majority of the experiments and contributed equally to the paper. LPS, FYL, WQX, QX and PH assisted with the experiments and data analysis. XMH provided clinical samples and technical advice. P-PW conceived the study, supervised the research, and wrote the manuscript. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Xiaoming Huang or Ping-Pui Wong.

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The research conducted with human participants followed the ethical guidelines set by the institutional and/or national research committee as well as the 1964 Helsinki declaration and its subsequent amendments or equivalent ethical standards. No animals were involved in any studies carried out by the authors of this article.

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Supplementary Information

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432_2023_4848_MOESM1_ESM.eps

Supplementary file1 Supplementary figure 1. The effectiveness of the prognostic risk model based on TEC-related gene expression signature was evaluated, and the differences in clinical characteristics between patients belonging to the high-risk and low-risk groups were examined. A–F Risk model could classify patients into high-risk and low-risk group effectively. (A, B) 3D-PCA analysis of the training or validation set. (C, D) t-SNE analysis of the training or validation set. (E, F) Analysis of patients’ survival statuses in the training or validation set. (H–M) Correlation study between the risk scores and the clinical features of cancer patients, including age (H), gender (I), clinical stage (J), T stage (K), N stage (L) or M stage (M). (N–P) ROC analyses showed that our TEC-related gene expression signature (N) was more effective than the other two external cohorts (i.e., Otsubo’s gene expression signature (O), Pitroda’s gene expression signature (P)) for predicting 1-, 3-, 5-year survival. (EPS 16783 KB)

432_2023_4848_MOESM2_ESM.eps

Supplementary file2 Supplementary figure 2. Comparison of the immune cell infiltration and activities of immune-related processes between patients in the high-risk and low-risk groups across different cancers. (A–H) Violin plots showed the differences in immune cell infiltration (left) and activities of immune-related processes (right) between patients in the high-risk and low-risk groups of either BRCA (A and B), HNSCC (C and D), LIHC (E and F) or NSCLC (G and H). Mean ± S.E.M. *P < 0.05, **P < 0.01, and ***P < 0.001. (A–H) One-way ANOVA. (EPS 4538 KB)

432_2023_4848_MOESM3_ESM.eps

Supplementary file3 Supplementary figure 3. The correlation between risk scores and IC-related gene expression in different cancers. (A–C) Correlation study between the risk scores and the expression of CD274 (A), CTLA4 (B) or PDCD1 (C) in BRCA. (D–F) Correlation study between the risk scores and the expression of CD274 (D), CTLA4 (E), or IDO1 (F) in HNSCC. (D–F) Relationship between the risk scores and the expression of CTLA4(G), HAVCR2 (H), or PDCD1 (I) in LIHC. (J, K) The association between the risk scores and the expression of CTLA4 (J) or HAVCR2 (K) in NSCLC. (EPS 12039 KB)

432_2023_4848_MOESM4_ESM.eps

Supplementary file4 Supplementary figure 4. The risk model, based on TEC-related prognostic gene expression signature, has the potential to predict appropriate treatment options for patients with cancer. (A–E) Correlation study between UBE2C expression and drug responses including Irofulven (A), imatinib (B), denileukin diftitox (C), megestrol acetate (D) or isotretinoin (E). (F–M) Correlation study between AURKB expression and drug responses, such as gemcitabine (F), cladribine (G), nelarabine (H), chlorambucil (I), cytarabine (J), hydroxyurea (K), thiotepa (L), triethylenemelamine (M). (N) Correlation study between AURKA expression and denileukin diftitox response. (O) The relationship between CDC20 expression and denileukin diftitox response. (P) Correlation study between CDCA8 expression and 6-thioguanine response. (A–P) Pearson correlation coefficient. (EPS 2349 KB)

432_2023_4848_MOESM5_ESM.eps

Supplementary file5 Supplementary figure 5. The expression of hub TEC-related genes determines cancer cell sensitivity to chemotherapy or targeted therapy. (A, B) CCK8 cell viability assay indicated that depletion of CDCA8/UBE2C by siRNA in CAL27 or A549 cells enhanced their sensitivity to gemcitabine and cytarabine even at different concentrations as compared to siNSC transfected cells. Bar charts show the relative cell viability in each group (n= 3 experimental repeats). Mean ± S.E.M. (C, D) Apoptosis assays indicated that silencing CDCA8/UBE2C in CAL27 or A549 cells increased gemcitabine-induced apoptosis as compared to siNSC transfected cells. Representative flow cytometry pictures of the annexin V-PI-stained cells in each group are given. Red number indicates the percentage of apoptotic cells in each group. (E–J) Analysis of correlation between CDCA8/UBE2C expression and overall survival in chemotherapy/targeted therapy treated breast, lung or liver cancer patients using the Kaplan–Meier (KM) plotter online database. Kaplan–Meier survival curve analysis revealed that patients with high CDCA8/UBE2C expression who received chemotherapy exhibited worse overall survival in cases of breast or lung cancer (E–G). Lung cancer patients with high CDCA8/UBE2C expression who received sorafenib had poor overall survival (I, J). *P < 0.05, **P < 0.01, and ***P < 0.001. (A, B) Student’s t test. (E–J) Log-rank (Mantel–Cox) test. (EPS 2553 KB)

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Zhuo, X., Huang, C., Su, L. et al. Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers. J Cancer Res Clin Oncol 149, 9635–9655 (2023). https://doi.org/10.1007/s00432-023-04848-2

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